Driving control apparatus for automated driving vehicle, stop target, and driving control system

ABSTRACT

A LiDAR sensor three-dimensionally scans laser light outward of the vehicle and receives reflected light. A LiDAR data analyzer groups three-dimensional points of the reflected light acquired by the LiDAR sensor into clusters. A precise docking control start determination unit compares shapes and reflectance distributions between at least one of the clusters obtained by the LiDAR data analyzer and a reference cluster representing a stop target to determine whether or not the at least one cluster includes the stop target.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to Japanese Patent Application No.2019-178781 filed on Sep. 30, 2019, which is incorporated herein byreference in its entirety including the specification, claims, drawings,and abstract.

TECHNICAL FIELD

The present specification discloses a driving control apparatus for anautomated driving vehicle, a stop target, and a driving control system.

BACKGROUND

Automated driving vehicles that automatically control steering and speedof vehicles are known. An automated driving vehicle includes varioussensors to estimate the vehicle location and recognize peripheralenvironments.

An automated driving vehicle includes, for example, a LiDAR (LightDetection and Ranging) sensor for measuring a distance to an obstacle inthe vicinity. A LiDAR sensor measures a distance to a peripheral objectusing laser light such as infrared rays. For example, a LiDAR sensorscans infrared laser light three-dimensionally (in the horizontal andvertical directions) to obtain three-dimensional point group data of thevicinity of the vehicle.

An automated driving vehicle further includes a camera that captures animage of the surrounding area. An image captured by the camera isanalyzed with a deep learning technique such as SSD (Single ShotMultibox Detector), for example, so that attributes of various objects(vehicles, passengers, structures, etc.) included in the image can berecognized.

By combining the three-dimensional point group data obtained by theLiDAR sensor with the image recognition by the camera, it is possible todetermine the distance of objects from the vehicle and attributes of theobjects.

If such an automated driving vehicle is public transportation such as apassenger bus, it is necessary to stop the vehicle beside a stop targetsuch as a bus stop, as disclosed in JP H11-3495 A and JP 2017-196962 A.This stopping control is also referred to as precise docking control.Precise docking control requires driving control for allowing a spacebetween the curb and an entrance step of the vehicle to be within aseveral centimeters, for example, to enable a wheelchair and the like toboard or exit smoothly. To execute precise docking control, it isnecessary to recognize a stop target ahead of the vehicle in thetravelling direction.

Precise docking control of a vehicle starts when a stop target isrecognized in a camera image, for example. In precise docking control,the three-dimensional point group data from the LiDAR sensor, forexample, are used to determine the distance between the recognized stoptarget and the vehicle, and the steering angle and the speed of thevehicle are controlled in accordance with the distance.

If it is possible to recognize a stop target by a sensor other than thecamera, in addition to the camera, an increase in the detection accuracyof the stop target is expected. Embodiments of the present disclosureare therefore directed toward providing an improved driving controlapparatus for an automated driving vehicle, a stop target, and a drivingcontrol system, capable of recognition of a stop target using a LiDARsensor.

SUMMARY

A driving control apparatus for an automated driving vehicle disclosedin the present specification includes a LiDAR sensor, an analyzer, and adetermination unit. The LiDAR sensor is configured tothree-dimensionally scan laser light outward of the vehicle and toreceive reflected light. The analyzer is configured to groupthree-dimensional points of the reflected light acquired by the LiDARsensor into clusters. The determination unit is configured to compareshapes and reflectance distributions between at least one of theclusters obtained by the analyzer and a reference cluster representing astop target to determine whether or not the at least one clusterincludes the stop target.

The above configuration is used to group the three-dimensional pointsacquired by the LiDAR sensor into clusters to enable recognition of thestop target by using the shape and the reflectance distribution of theclusters.

In the above configuration, the reference cluster may have a stripeconfiguration including repeated patterns of a plurality of regions inwhich adjacent regions have different reflectances with respect to thelaser light.

The above configuration facilitates discrimination of the stop targetfrom other objects, resulting in increased accuracy of recognition ofthe stop target.

In the above configuration, the reference cluster may include, as thestripe configuration, a high reflectance region having a relatively highreflectance with respect to the laser light and a low reflectance regionhaving a relatively low reflectance with respect to the laser light. Thehigh reflectance region and the low reflectance region may bealternately arranged repeatedly.

In the above configuration, the stripe configuration includes two typesof zones: the high reflectance region and the low reflectance region.This increases a difference in reflectance between the regions (i.e.,enhances the contrast), thereby facilitating recognition of the stoptarget from a distant place.

In the above configuration, the reference cluster may include, as thestripe configuration, a horizontal stripe configuration including thehigh reflectance region and the low reflectance region both extendinghorizontally. The high reflectance region and the low reflectance regionmay be alternately disposed repeatedly in a vertical direction.

The stop target typically has a vertically elongated configuration withthe horizontal dimension smaller than the vertical dimension. When sucha stop target and the corresponding reference cluster have a horizontalstripe configuration including the high reflectance region and the lowreflectance that are alternately arranged repeatedly in the verticaldirection, each stripe can have an increased width. This facilitatesrecognition of the stop target from a distant place accordingly.

In the above configuration, the reference cluster may have a surfaceshape of 0° to 180° of a circular cylindrical side face.

The stop target having a circular cylindrical shape reduces variationsof the projection area caused by the imaging angle of the stop target.This requires that the reference cluster corresponding to the stoptarget should have only a surface shape of 0° to 180° of the circularcylindrical side face; that is, only a surface shape of the circularcylindrical side face as viewed from the front, eliminating the need tochange the shape of the reference cluster depending on the imagingangle, for example.

A stop target disclosed in the specification is a stop target for anautomated driving vehicle comprising a LiDAR sensor configured tothree-dimensionally scan laser light outward of the vehicle and toreceive reflected light. The stop target has a stripe configurationincluding repeated patterns of a plurality of regions in which adjacentregions have different reflectances with respect to the laser light.

A driving control system disclosed in the specification includes a stoptarget, and an automated driving vehicle to stop beside the stop target.The stop target has a stripe configuration including repeated patternsof a plurality of regions in which adjacent regions have differentreflectances with respect to laser light. The automated driving vehicleincludes a LiDAR sensor, an analyzer, and a determination unit. TheLiDAR sensor is configured to three-dimensionally scan the laser lightoutward of the vehicle and to receive reflected light. The analyzer isconfigured to group three-dimensional points of the reflected lightacquired by the LiDAR sensor into clusters. The determination unit isconfigured to compare shapes and reflectance distributions between atleast one of the clusters obtained by the analyzer and a referencecluster representing a stop target to determine whether or not the atleast one cluster includes the stop target.

The driving control apparatus for an automated driving vehicle, the stoptarget, and the driving control system disclosed in the specificationenable recognition of a stop target using a LiDAR sensor.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the present disclosure will be described by reference tothe following figures, wherein:

FIG. 1 is a perspective view illustrating an outer appearance of avehicle;

FIG. 2 is a plan view illustrating an outer appearance of a vehicle;

FIG. 3 is an enlarged perspective view of a sensor unit and itsperiphery on a front face of the vehicle;

FIG. 4 illustrates an irradiation range of a LiDAR unit;

FIG. 5 is a hardware structural view illustrating a configuration of adriving control apparatus for an automated driving vehicle according toan embodiment;

FIG. 6 illustrates functional blocks of a control unit;

FIG. 7 illustrates a stop target according to an embodiment;

FIG. 8 illustrates a reference cluster corresponding to a stop targetaccording to an embodiment;

FIG. 9 illustrates another example stop target according to the presentembodiment;

FIG. 10 is a diagram for explaining a change in the projection area ofthe stop target depending on the angle of the stop target;

FIG. 11 illustrates another example stop target according to anembodiment;

FIG. 12 illustrates another example stop target according to anembodiment;

FIG. 13 illustrates a precise docking control start determination flowperformed by a driving control apparatus for an automated drivingvehicle according to an embodiment;

FIG. 14 illustrates an example image captured by a camera;

FIG. 15 illustrates three-dimensional point group data provided by aLiDAR sensor, which correspond to the scene illustrated in FIG. 14 ;

FIG. 16 illustrates a clustering process of three-dimensional pointgroup data;

FIG. 17 illustrates an image after recognition of objects;

FIG. 18 illustrates another example stop target according to anembodiment; and

FIG. 19 illustrates another example stop target according to anembodiment.

DESCRIPTION OF EMBODIMENTS

Vehicle Configuration

The configuration of a vehicle 10 including a driving control apparatusaccording to the present embodiment will be described by reference tothe drawings. In FIGS. 1 to 4 , a direction along the length of avehicle body is indicated by an axis denoted with a sign FR, a directionalong the width of the vehicle is indicated by an axis denoted with asign LH (Left Hand), and a direction along the vehicle height isindicated by an axis denoted with a sign UP.

The lengthwise axis FR and the widthwise axis LH both extend along thehorizontal direction, and the vehicle height axis UP extends along thevertical direction. The forward direction of the vehicle body lengthwiseaxis FR corresponds to the frontward direction of the vehicle body; theforward direction of the widthwise axis LH corresponds to the leftwardin the vehicle width direction; and the forward direction of the vehicleheight axis UP corresponds to the upward direction. These three axes areorthogonal to each other.

In the following description, unless otherwise specified, frontward inthe vehicle body lengthwise direction is simply referred to as“frontward” or “front”, rearward in the vehicle body lengthwisedirection is simply referred to as “rearward” or “rear”, upward in thevehicle height direction is simply referred to as “upward”, and downwardin the vehicle height direction is simply referred to as “downward”.

FIG. 1 is a perspective view illustrating an outer appearance of thevehicle 10. Specifically, FIG. 1 illustrates a front face (front) and aleft side face of the vehicle 10.

The vehicle 10 is an automated driving vehicle having automated drivingfunctions. The vehicle 10 is capable of automated driving from level 0(fully manual) to level 5 (fully autonomous) based on the automateddriving standard of the Society of Automotive Engineers (SAE), forexample. According to the SAE standard, for example, a driver'smanipulation is at least partially required from level 0 to level 3. Inlevel 4 (highly automated driving), full automated driving which doesnot require driver's manipulation operation is executed within a limitedarea (e.g., within a bus operation route). In level 5, automated driving(full autonomous operation) which does not require drivers under anyconditions is executed.

The vehicle 10 is used as a passenger bus which travels along apredetermined route according to automated driving within a specificsite with passengers being on board in the vehicle interior. Duringoperation, the automated driving level is set to SAE level 4, forexample.

The vehicle 10 is an electric car that uses a rotary electric machine asa drive source, for example. The vehicle 10 includes a main battery (notshown) installed to supply electric power to the rotary electricmachine. The vehicle 10 is not limited to an electric car, and may be ahybrid vehicle including an internal combustion engine (engine) and arotary electric machine as drive sources.

Referring to FIG. 1 , the vehicle 10 includes entrance/exit doors 12 inthe center of the left side face. The entrance/exit doors 12 are, forexample, slidable double doors that move lengthwise of the vehicle in aslidable manner to open and close. The vehicle 10 is configured totravel on the left side of the road.

The vehicle 10 includes, on its front face, a pair of head lamps 14(headlights). The vehicle 10 further includes, between the pair of headlamps 14, a destination and mode display 16 showing letters indicating adestination and an operation mode (e.g., automated driving or manualdriving), for example. The destination and mode display 16 further showsmessage to passengers, such as “After You”, when the vehicle 10 stopsbefore a pedestrian crossing.

The vehicle 10 further includes a plurality of sensors to enableautomated driving. Referring to FIG. 1 and FIG. 2 , the vehicle 10includes, on its front face, rear face, and side faces, sensor units 30(30A to 30D). The vehicle 10 further includes clearance sensors 20 atthe four corners of the vehicle 10.

The clearance sensors 20 may be sonar sensors, for example, and measurethe distance between the vehicle 10 and surrounding objects. Inexecuting precise docking which will be described below, for example,the clearance sensors 20 measure the distance between the vehicle 10 andthe curb.

FIG. 3 illustrates the sensor unit 30A disposed on the front face of thevehicle 10. The other sensor units 30B, 30C, and 30D are similarlyconfigured. The sensor unit 30A protrudes from the vehicle front facetoward outside the vehicle; that is, protrudes frontward of the vehicle.The sensor unit 30 includes a camera 34, a LiDAR sensor 36, and a casing32 that houses these elements.

The casing 32 protects the camera 34 and the LiDAR sensor 36 againstwind and rain or obstacles, for example, while securing their field ofview. The casing 32 is composed of an optically transparent resinmaterial, for example. The casing 32 is configured in a semicylindricalshape protruding from the vehicle front face toward the vehicle outside(frontward of the vehicle), for example.

The LiDAR sensor 36 is a sensor unit for automated driving which employsLiDAR; that is, a technique for measuring a distance to a surroundingobject using laser light. The LiDAR sensor 36 includes an emitter 36Athat emits infrared laser light outward, a receiver 36B that receivesthe reflected light, and a motor 36C that rotates the emitter 36A andthe receiver 36B.

An irradiation plane of the emitter 36A and a receiving plane of thereceiver 36B are parallel to each other and are aligned in the vehiclewidth direction and the vehicle length direction. The emitter 36A emitsinfrared laser light outward of the vehicle; that is, toward the regionahead of the vehicle 10. The emitter 36A may include a pulse laser lightsource that emits laser light of around 905 nm. The laser light emittedfrom the emitter 36A impinges onto an object located ahead of thevehicle 10, and the reflected light is received by the receiver 36B. Thedistance between the reflection point and the receiver 36B is determinedbased on time elapsed from light emission from the emitter 36A to lightreception by the receiver 36B.

The motor 36C allows the emitter 36A and the receiver 36B to rotateabout its vertical axis (UP axis) serving as a rotary axis. The motor36C may be a servo motor, for example. The motor 36C allows the emitter36A and the receiver 36B to scan horizontally, so that range formeasuring the distance to an object in the region ahead of the vehicle10 expands in the horizontal direction. An electro-magnetic mirror maybe used, in place of the motor 36C, to perform the horizontal scanning.

The emitter 36A and the receiver 36B may also be line sensors extendingin the vertical direction (height direction). For example, a pluralityof light sources (e.g., laser elements) of the emitter 36A are alignedin the vertical direction and a plurality of light receiving elements ofthe receiver 36B are also aligned in the vertical direction. The emitter36A and the receiver 36B are arranged radially in the vertical directionsuch that laser light can be emitted radially and the reflected lightcan be received. Such an arrangement enables acquisition of the rangedata by vertical scanning.

As described above, the LiDAR sensor 36 scans laser light outward of thevehicle in the horizontal and vertical directions; that is,three-dimensionally, and receives the reflected light, as illustrated inFIG. 4 . Based on the irradiation angle and the light receiving angle(including the horizontal and vertical angles) and the time elapsed fromlaser light irradiation to reception of light, three-dimensionalinformation of the reflection points; that is, the distance from thevehicle to the reflection points, can be obtained three-dimensionally,as the range data. Then, three-dimensional points as illustrated in FIG.15 are determined by two-dimensionally obtaining the reflection pointsincluding the three-dimensional information. In FIG. 15 , the reflectionpoints are indicated with circles.

Each reflection point includes, in addition to the three-dimensionalinformation, its reflectance data. As will be described below, inrecognizing a stop target 70, a reflectance distribution of clusteredthree-dimensional points is compared with a reflectance distribution ofa reference cluster 80 representing the stop target 70.

The camera 34 captures an image of the same view as the LiDAR sensor 36.The camera 34 includes an image sensor such as a CMOS sensor or a CCDsensor, for example. The image captured by the camera 34 (capturedimage) is used for automated driving control. For example, the imagecaptured by the camera 34 is analyzed to detect objects included in theimage and recognize the attribute of the objects, as described below.

FIG. 5 illustrates a driving control apparatus of an automated drivingvehicle according to the present embodiment. The driving controlapparatus includes the camera 34, the LiDAR sensor 36, a dynamic map 22,a navigation system 24, a steering mechanism 26, a braking mechanism 27,a driving mechanism 28, and a control unit 40. The driving controlapparatus is mounted on the vehicle 10.

The dynamic map 22 is a three-dimensional map that stores locations andthree-dimensional configurations of roads, footpaths, surroundingstructures, traffic signals, and stop lines, for example. The navigationsystem 24 performs positioning using an artificial satellite, and a GNSS(Global Navigation Satellite System) is used, for example. As will bedescribed below, the navigation system 24 and the dynamic map 22 areused to enable estimation of the location of the vehicle with accuracywithin a range of positioning errors of the artificial satellite.

The steering mechanism 26 is used to manipulate a steering wheel, forexample, and includes a steering motor, for example. When a steeringinstruction is supplied from the control unit 40 to the steering motor,the steering angle of the vehicle 10 is controlled.

The braking mechanism 27 is used to manipulate a brake mechanism, andincludes, for example, an actuator for a master cylinder that controlsthe oil pressure of the brake. Upon receiving a braking instruction fromthe control unit 40 by the actuator, the vehicle 10 decelerates.

The driving mechanism 28 is used to control driving force of a rotaryelectric machine which is a drive source of the vehicle 10, andincludes, for example, an inverter that controls the driving force ofthe rotary electric machine. When a driving instruction is supplied fromthe control unit 40 to the inverter, the driving force of the vehicle 10is controlled.

The control unit 40 may be an electronic control unit (ECU) of thevehicle 10, for example, and is composed of a computer. The control unit40 includes an input/output controller 41 that controls input/output ofdata. The control unit 40 further includes, as operation elements, a CPU42, a GPU (Graphics Processing Unit) 43, and a DLA (Deep LearningAccelerator) 44. The control unit 40 also includes, as storage units, aROM 45, a RAM 46, and a hard disk drive 47 (HDD). These structuralelements are coupled to an internal bus 48.

At least one of the ROM 45 and the hard disk drive 47 which are storagedevices stores a program for executing automated driving controlincluding precise docking control. At least one of the ROM 45 and thehard disk drive 47 further stores a program for executing a precisedocking control start determination flow as illustrated in FIG. 13 .

To execute programs of the automated driving control and the precisedocking control start determination flow, the control unit 40 includesfunction blocks as illustrated in FIG. 6 , including an imaging dataanalyzer 50, a LiDAR data analyzer 52, a vehicle location estimator 54,a precise docking control start determination unit 56, a referencecluster memory unit 58, and an automated driving controller 60.Operation of these function blocks will be described below.

Stop Target

FIG. 7 to FIG. 12 , FIG. 18 , and FIG. 19 illustrate stop targets 70 andcorresponding reference clusters 80. The stop target 70 and the drivingcontrol apparatus mounted on the vehicle 10 constitute a driving controlsystem for an automated driving vehicle according to the presentembodiment.

FIG. 7 , FIG. 8 , FIG. 9 , FIG. 11 , FIG. 12 , FIG. 18 , and FIG. 19each show a vertical axis V and a horizontal axis H. The vertical axis Vis parallel to the vehicle height axis UP of the vehicle, and thehorizontal axis H extends within the same plane (within the horizontalplane) as the lengthwise axis FR and the widthwise axis LH.

Referring to FIG. 7 , the stop target 70 is a so-called bus stop and isa target which indicates a stop position of the vehicle 10. The outerface (exposed face) of the stop target 70 may have a stripeconfiguration including repeated patterns of a plurality of regions inwhich adjacent regions have different reflectances with respect to laserlight.

The outer face (exposed face) of the stop target 70 includes, as astripe configuration, high reflectance regions 72 with relatively highreflectance with respect to the infrared laser light emitted from theLiDAR sensor 36 and low reflectance regions 74 with relatively lowreflectance, which are alternately arranged in a repeated manner, forexample. In other words, the outer face (exposed face) includes aplurality of pairs, each including the high reflectance region 72 andthe low reflectance region 74.

The stripe configuration formed of two types of zones, the highreflectance region 72 and the low reflectance region 74, can assume asignificant difference in reflectance among the regions (that is, canhave high contrast). This facilitates recognition of the stop targetfrom a distant place.

The exposed face of the stop target 70 may include a horizontal stripeconfiguration, for example. More specifically, the exposed face includesthe high reflectance region 72 and the low reflectance region 74 bothextending in the horizontal direction and arranged alternately in thevertical direction.

Typically, a bus stop has a vertical dimension L1 which is greater thana horizontal dimension R1, as illustrated in FIG. 7 . The horizontalstripe configuration in which stripe patterns are deployed along thelongitudinal direction has a greater number of patterns than the numberof patterns in the vertical stripe configuration in which stripepatterns are deployed along the lateral direction, and can have anincreased width (stripe width) for each stripe.

The stop target having an increased number of stripe patterns can beeasily extracted from among surrounding objects. Further, the stoptarget 70 with a greater stripe width can be recognized from a distantplace.

The stripe width is determined in light of the specifications of theLiDAR sensor 36 (see FIG. 6 ) and the start point of the precise dockingcontrol. For example, the vertical angle resolution θ of the LiDARsensor 36 and the distance r with respect to the stop target 70 at whichthe precise docking control starts are used to determine a space zbetween the laser light beams in the vertical direction at the distancer from the following equation: z=2r×tan(θ/2).

To obtain the reflection points in the high reflectance region 72 andthe low reflectance region 74 of the stop target 70 from the distance r,the stripe width needs to have the minimum value w_(min) that is equalto or greater than z. Assuming that the vertical angle resolution θ is0.1°, and the distance r from the vehicle 10 (more specifically, theLiDAR sensor 36) to the stop target 70, where the precise dockingcontrol starts, is 100 m, for example, z≈17 cm. Therefore, the minimumvalue w_(min) of the stripe width is 17 cm. The high reflectance region72 and the low reflectance region 74 may have an equal stripe width, andthe stripe widths may also be equal among the patterns.

The maximum value W_(max) of the stripe width is determined based on theheight L1 of a pattern plane of the stop target 70 where the stripeconfiguration can be formed and the number m of repetition of patternsformed by pairs of the high reflectance region 72 and the lowreflectance region 74. For example, the stripe width w, the patternplane height L1, and the pattern repetition number m have a relationshipof 2w×m≤L1. Assuming that the pattern repetition number is 5 and thepattern plane height L1 is 2 m, w≤20 cm, so that the maximum valueW_(max) of the stripe width is 20 cm.

The stripe width of the high reflectance region 72 and the lowreflectance region 74 may vary for each pair of the high reflectanceregion 72 and the low reflectance region 74, or may be equal for all thepatterns. However, if it is difficult to recognize the difference of thestipe widths due to the resolution of the LiDAR sensor 36, the stripewidths of all of the high reflectance regions 72 and the low reflectanceregions 74 may be equal.

The stop target 70 is made of a resin plate, for example, to which atape reflector (reflective tape) is attached. A resin reflector may beused in place of the resin plate. A region of the stop target 70 wherethe tape reflector is attached is the high reflectance region 72, and aregion without the tape reflector is the low reflectance region 74.

The reflectance of the high reflectance region 72 and the reflectance ofthe low reflectance region 74 are determined based on the difference inreflectance at the distance r to the stop target 70, where the precisedocking control starts, for example. The difference in reflectancebetween adjacent reflectance regions, such as the difference inreflectance between the high reflectance region 72 and the lowreflectance region 74 in the example illustrated in FIG. 7 , should be10% or greater, for example, when the LiDAR sensor 36 emits infraredlaser light to the stop target 70 at the distance r.

Each of the reflectance regions (the high reflectance region 72 and thelow reflectance region 74 in the example illustrated in FIG. 7 ) may bein a color other than black which is known to have high absorptance ofinfrared ray. If the LiDAR data analyzer 52 (see FIG. 6 ) determinesreflectance 0%, as will be described below, there is a possibility thatthe stop target 70 is segmented into a plurality of clusters. Forexample, if the reflectance of the low reflectance region 74 is around0%, it is possible that the high reflectance regions 72 may be groupedinto different clusters. The high reflectance regions 72 and the lowreflectance regions 74 having colors other than black inhibitsegmentation of the stop target 70 into a plurality of clusters asdescribed above.

The stop target 70 may have a circular cylindrical shape, as illustratedin FIG. 7 . The stop target 70 having a circular cylindrical shapeinhibits variation in a projection area caused by the imaging angle ofthe stop target 70. Each of the high reflectance region 72 and the lowreflectance region 74 may be disposed along the entire circumference ofthe side face of the circular cylinder.

FIG. 8 illustrates a reflectance distribution obtained when the stoptarget 70 illustrated in FIG. 7 is irradiated with infrared laser. Thiscorresponds to the shape of the stop target 70; that is, the frontalshape of the stop target 70 viewed from the LiDAR sensor 36, and to thereflectance distribution on the plane, when the infrared laser light isscanned three-dimensionally (horizontally and vertically) by the LiDARsensor 36. The reflectance distribution and the shape illustrated inFIG. 8 are prestored in the reference cluster memory unit 58 (see FIG. 6) as the reference cluster 80 representing the stop target.

FIG. 8 shows the reflection points by circles; an unhatched circlerepresents a reflection point with a relatively high reflectance and ahatched circle represents a reflection point with a relatively lowreflectance. A high reflectance region 82 in the reference cluster 80corresponds to the high reflectance region 72 in the stop target 70.Similarly, a low reflectance region 84 in the reference cluster 80corresponds to the low reflectance region 74 in the stop target 70.

Thus, the reference cluster 80 also has a stripe configuration includingrepetition of patterns of a plurality of regions with the reflectancewith respect to the laser light being different between adjacentregions. FIG. 8 , for example, includes a stripe configuration includingthe high reflectance regions 82 and the low reflectance regions 84 thatare alternately arranged repeatedly. More specifically, in the cluster80, the high reflectance regions 82 and the low reflectance regions 84that extend horizontally are disposed alternately in the verticaldirection in a repeated manner.

The reference cluster 80 also has, corresponding to the stop target 70illustrated in FIG. 7 , a surface shape of a side face of the circularcylinder as viewed from the front. Referring to FIG. 7 , for example,the surface shape as viewed from front represents a surface shape of theside face of 0° to 180° of the circular cylinder, as indicated by aregion A 1.

As described above, the stop target 70 having a circular cylindricalshape reduces variation of the projection area depending on the imagingangle of the stop target 70. Therefore, the surface shape of the stoptarget 70 of 0° to 180° of the circular cylinder side face; that is, thesurface shape of the circular cylinder side face as viewed from thefront, is itself sufficient for the reference cluster 80 of the stoptarget 70; it is not necessary to alter the shape of the referencecluster 80 for each imaging angle, for example.

FIG. 9 illustrates another example stop target 70 according to thepresent embodiment. In this example, the stop target 70 has a prismpillar shape. The stop target 70 is a rectangular parallelepiped, forexample, and has a plane with a relatively larger area (a plane inparallel to axis H1) facing the vehicle 10.

When such a stop target 70 having a prism pillar shape is set on afootpath, the projection width (W1, W2) of the stop target 70 variesdepending on the angle (imaging angle) of the LiDAR sensor 36 withrespect to the stop target 70, as illustrated in FIG. 10 . Therefore, aplurality of reference clusters 80 having different projection widths inaccordance with the imaging angle are stored in the reference clustermemory unit 58 (see FIG. 6 ).

FIG. 11 illustrates an example stop target 70 with a time table plate 76attached thereto. The time table plate 76 shows times indicatingscheduled arrival time of the vehicle 10. The time table plate 76 isdisposed to avoid the high reflectance region 72 and the low reflectanceregion 74 of the stop target 70. The position of the time table plate 76is determined such that the high reflectance region 72 and the lowreflectance region 74 do not overlap the time table plate 76 along adirection in which the footpath extends.

FIG. 12 illustrates a still another example stop target 70 according tothe present embodiment. In this example, the stop target 70 includes, inits center part in the height direction, a narrow part 78 having arelatively smaller diameter as compared to regions above and below. Sucha unique shape makes it easy to discriminate the stop target 70 fromsurrounding structures, passengers, and other objects.

When the vehicle 10 approaches the stop target 70, for example, thelocation of the stop target 70 can be detected with higher accuracy byusing the narrow part 78 with a relatively smaller diameter as areference than by using a part with a relatively larger diameter as areference. Further, the narrow part 78 may be set at a height levelwhich is equal to that of the LiDAR sensor 36 of the vehicle 10.

The diameter R2 of the narrow part 78 is determined, using the distancer from the vehicle 10 (more specifically, from the LiDAR sensor 36)where the precise docking control starts to the stop target 70 and thehorizontal resolution α of the LiDAR sensor 36, as R2≥2r×tan(α/2).

While FIG. 7 , FIG. 9 , FIG. 11 , and FIG. 12 illustrate the stripeconfiguration of the stop target 70 including the high reflectanceregion 72 and the low reflectance region 74, and the correspondingreference cluster 80 includes the stripe configuration having the highreflectance region 82 and the low reflectance region 84, the stop target70 and the reference cluster 80 are not limited to these examples.

Specifically, as the stop target 70 and the reference cluster 80 areonly required to have a stripe configuration including repeated patternsof a plurality of regions with adjacent regions having differentreflectances with respect to laser light, they may have a stripeconfiguration of three-color stripes, as illustrated in FIG. 18 , forexample. In this example, the stop target 70 has a stripe configurationincluding an intermediate reflectance region 73 between the highreflectance region 72 and the low reflectance region 74. Therelationship among the reflectances of these regions is as follows: highreflectance region 72>intermediate reflectance region 73>low reflectanceregion 74. Accordingly, the reference cluster 80 also has a stripeconfiguration including an intermediate reflectance region between thehigh reflectance region 82 and the low reflectance region 84.

In the stop target 70, a single pattern may include an identicalreflectance region a plurality of times. For example, as illustrated inFIG. 19 , the stop target may have a stripe configuration includingrepeated patterns each formed of four reflectance regions including thehigh reflectance region 72, the intermediate reflectance region 73, thelow reflectance region 74, and the intermediate reflectance region 73 inthis order. The reference cluster 80 may accordingly have a stripeconfiguration including repeated patterns each formed of fourreflectance regions including the high reflectance region 82, theintermediate reflectance region 73, the low reflectance region 84, andthe intermediate reflectance region 73 in this order.

Automated Driving Control

Referring to FIG. 6 , automated driving control performed by the drivingcontrol apparatus of the vehicle 10 will be described. As automateddriving control is known, the following description especially refers tofeatures related to the precise docking control start determination flowwhich will be described below.

The dynamic map 22 which is a three-dimensional map and the navigationsystem 24 are used to perform location estimation of the vehicle 10. Thenavigation system 24 which is a satellite positioning system transmitsto the vehicle location estimator 54 information concerning latitude andlongitude of vehicle 10. Further, the dynamic map 22 transmits to thevehicle location estimator 54 map data of a location corresponding tothe information of the latitude and longitude of the vehicle 10.Consequently, the location of the vehicle 10 within a range of errors ofsatellite positioning (e.g., ±10 cm) is estimated.

The vehicle location estimator 54 further acquires three-dimensionalpoint group data (scan data) of the vehicle 10 and its peripheral regionfrom the LiDAR sensor 36. FIG. 15 illustrates example three-dimensionalpoint group data. The location of the vehicle 10 is estimated with anerror which is less than an error of satellite positioning, by matchingthe three-dimensional point group data and the three-dimensional mapdata from the dynamic map 22.

The imaging data analyzer 50 acquires a captured image illustrated inFIG. 14 , which is captured by the camera 34. The imaging data analyzer50 further detects objects within the image according to a known deeplearning method such as SSD (Single Shot Multibox Detector) and YOLO(You Only Look Once) using supervised learning, and recognizes theirattributes (vehicles, passengers, structures, and the like). Forexample, as illustrated in FIG. 17 , the imaging data analyzer 50recognizes from the captured image the stop target 70, vehicles 90, afootpath surface 92, and a traffic lane 94.

The LiDAR data analyzer 52 acquires the three-dimensional point groupdata (see FIG. 15 ) from the LiDAR 36. The LiDAR data analyzer 52further executes clustering for grouping the three-dimensional pointsinto at least one cluster.

While FIG. 15 shows the vehicles and the structures with dashed lines sothat the correspondence between the point group and the object can beunderstood, these dashed lines are not actually shown. The LiDAR dataanalyzer 52 therefore segment the three-dimensional points into certainpoint groups for clustering. For clustering, known methods may be used;for example, there is used Euclidean clustering that uses the Euclideandistance of each reflection point for grouping points with similarEuclidean distances into a cluster. In the example illustrated in FIG.16 , for example, the three-dimensional points are grouped into clustersCL1 to CL13 by clustering.

The automated driving controller 60 uses the captured image analyzed bythe imaging data analyzer 50 and object information included in theimage, the clustered three-dimensional point group data analyzed by theLiDAR data analyzer 52, and the vehicle location information estimatedby the vehicle location estimator 54, to perform driving control of thevehicle 10.

By superposing the captured image and the three-dimensional point groupdata, for example, there can be determined information concerning anobject, such as what type of attribute it has and its distance from thevehicle 10. Using this superposing information, the automated drivingcontroller 60 controls the steering mechanism 26, the braking mechanism27, and the driving mechanism 28.

In precise docking control, for example, the steering angle and speed ofthe vehicle 10 are determined based on the distance to the stop target70, and the automated driving controller 60 transmits a correspondingsteering instruction, braking instruction, and driving instruction tothe steering mechanism 26, the braking mechanism 27, and the drivingmechanism 28, respectively. This control finally enables the vehicle 10to stop beside the stop target 70.

Precise Docking Control Start Determination Flow

FIG. 13 illustrates a precise docking control start determination flowaccording to the present embodiment. Precise docking control of avehicle is triggered by recognition of the stop target 70. According tothe illustrated flow, whether to start the precise docking control isdetermined based on whether or not the stop target 70 has beenrecognized.

Further, as will be described below, according to the illustrated flow,in recognizing the stop target 70, analysis of the three-dimensionalpoint group data using the LiDAR sensor 36, unlike image recognitionusing the captured image from the camera 34, is used to recognize thestop target 70.

The flow illustrated in FIG. 13 starts when the vehicle 10 enters a “busstop vicinity area”, which is an area in the vicinity of the stop target70. The bus stop vicinity area may be a region within a 500 m radius ofthe stop target 70.

Under automated driving, for example, a destination and a route to thedestination are determined in advance. The location of the stop target70 along the route is further determined using the dynamic map 22. Inaddition, based on the determined location of the stop target 70 (itslocation on the dynamic map) and the vehicle location estimationdescribed above, the distance between the vehicle 10 and the stop target70 can be estimated.

However, as this estimation is a rough location estimation includingerrors based on the position information accuracy of the dynamic map 22and errors of satellite positioning, the bus stop vicinity area is setto the order greater than the order of these errors. For example, if theerrors of the dynamic map 22 and the satellite positioning are of theorder of 10 cm, the bus stop vicinity area is set to the order of 100 m,which can disregard these errors.

Upon starting the precise docking control start determination flow, theLiDAR data analyzer 52 acquires three-dimensional point group data fromthe LiDAR sensor 36 (S10). The LiDAR data analyzer 52 further executesclustering of the acquired three-dimensional point group data (S12). Theclustered three-dimensional point group data are transmitted to theprecise docking control start determination unit 56.

The precise docking control start determination unit 56 compares shapesand reflectance distributions between at least one cluster acquired bythe LiDAR data analyzer 52 and the reference cluster 80 that representsthe stop target 70 to determine whether or not the at least one clusterincludes the stop target 70. Specifically, the precise docking controlstart determination unit 56 confirms the number n of the obtainedclusters (S14), and extracts the first cluster (k=1) from the pluralityof clusters (S16). For example, cluster CL1 in FIG. 16 is extracted.

The precise docking control start determination unit 56 then comparesthe k-th cluster CL_k with the reference cluster 80 (see FIG. 8 ) storedin the reference cluster memory unit 58 to determine whether or notthese clusters match (S18 to S24).

The degree of match based on this comparison may be determined using aknown method such as template matching, for example. Specifically, thereference cluster 80 is used as a template.

First, the shapes are compared between the cluster CL_k and thereference cluster 80 (S18 and S20). In this comparison and in comparingthe reflectance distributions in the later stage, the reference cluster80 may be corrected. For example, the precise docking control startdetermination unit 56, based on the distance between the cluster CL_kand the LiDAR sensor 36 when the three-dimensional point group data wereacquired, enlarges or reduces the reference cluster 80. At this time,the reflection points which may be included in the stripe widths of thehigh reflectance region 82 and the low reflectance region 84,respectively, of the reference cluster 80 are increased or decreased.

If in step S20 the shape of the cluster CL_k and the shape of thereference cluster 80 do not match, it is determined that the clusterCL_k does not correspond to the stop target 70. Therefore, afterdetermining whether or not the cluster counter k reaches the maximumvalue n (S26), the cluster counter k, not reaching the maximum value n,is incremented (S28) and the next cluster is set as a comparison target.

If in step S20 it is determined that the shape of the cluster CL_k andthe shape of the reference cluster 80 match, the two-dimensionalreflectance distributions of these clusters are further compared (S22and S24). For example, the arrangement pattern of the reflection pointregions having relatively high reflectance and the reflection pointregions having relatively low reflectance in the cluster CL_k and thearrangement pattern of the high reflectance region 82 and the lowreflectance region 84 in the reference cluster 80 are compared.

If it is determined that the cluster CL_k and the reference cluster 80do not match according to comparison of the reflectance distribution, itis then determined that the cluster CL_k does not correspond to the stoptarget 70. Therefore, after determining whether or not the clustercounter k reaches the maximum value n (S26), the cluster counter k, notreaching the maximum value n, is incremented (S28), and the next clusteris set as a comparison target.

If in step S24 it is determined that the reflectance distribution of thecluster CL_k and the reflectance distribution of the reference cluster80 match, the precise docking control start determination unit 56outputs a start instruction for the precise docking control to theautomated driving controller 60 (S34). This changes the driving controlfor the vehicle 10 from normal travelling control to the precise dockingcontrol to the stop target 70.

If in step S26 it is determined that all of the clusters, clusters CL_1to CL_n, in the three-dimensional point group data do not correspond tothe reference cluster 80, the precise docking control startdetermination unit 56 determines whether or not the vehicle 10 haspassed the bus stop vicinity area (S36). This determination is performedusing the vehicle location estimation described above, for example.

If in step S36 it is determined that the vehicle 10 stays in the busstop vicinity area, the precise docking control start determination unit56 resets the number n of clusters and the cluster counter k (n=0, andk=1, for example) (S30). Further, the LiDAR data analyzer 52 acquiresnew three-dimensional point group data from the LiDAR sensor 36 toupdate the three-dimensional point group data to be analyzed (S32). Theupdated three-dimensional point group data are clustered by the LiDARdata analyzer 52 as described above (S12).

If in step S36 it is determined that the vehicle 10 has passed the busstop vicinity area, this means that the stop target 70 has not beendetected (recognized) over the entire region of the bus stop vicinityarea. In this case, the precise docking control start determination unit56 executes fault processing (S38). For example, the precise dockingcontrol start determination unit 56 notifies occupants within thevehicle or a remotely located operator of a message indicating that thestop target 70 could not be recognized.

As described above, the driving control apparatus according to thepresent embodiment enables recognition of the stop target 70 using theLiDAR sensor 36 alone; that is, without using image recognition by thecamera 34.

The driving control apparatus according to the present embodiment iscapable of both recognition of the stop target 70 by the camera 34 andrecognition of the stop target 70 by the LiDAR sensor 36. Therefore, inaddition to the recognition of the stop target 70 by the LiDAR sensor 36alone as illustrated in FIG. 13 , the recognition of the stop target 70by the camera 34 may also be used to increase the detection accuracy ofthe stop target 70.

For example, the recognition of the stop target 70 by the LiDAR sensor36 and the recognition of the stop target 70 by the camera 34 may beused under AND condition or OR condition. When the AND condition isused, for example, the fault processing described above may be executedif the stop target 70 cannot be recognized by at least one of the LiDARsensor 36 or the camera 34. On the other hand, when the OR condition isused, the precise docking control may be started if the stop target 70can be recognized by at least one of the LiDAR sensor 36 or the camera34.

While, in the above example, the exposed face of the stop target 70 hasa stripe configuration, this may raise a possibility that a passengerwearing a shirt with horizontal stripe patterns, for example, isconfused with the stop target 70. In such a case, cluster tracking ofthe automated driving technology is used to determine whether or not thecluster which is recognized as the stop target 70 is moving. Ifdetermined to not be moving, the cluster is determined to be the stoptarget 70. A determination flow using such a tracking may be added tothe precise docking control start determination flow illustrated in FIG.13 .

In addition to the stop target 70, an auxiliary target may be disposedalong the curb of the footpath. The auxiliary target, similar to thestop target 70, may have a configuration which is recognizable by theLiDAR sensor 36. Specifically, the auxiliary target may have ahorizontal stripe configuration including repeated patterns of the highreflectance region 72 and the low reflectance region 74 which aredisposed alternately in the vertical direction.

Recognizing the stop target 70 and the auxiliary target disposed alongthe curb enables recognition of the borderline between the footpath andthe roadway. For example, a straight line connecting the stop target 70and the auxiliary target can be used to recognize the curb which is theborderline between the footpath and the roadway. The present disclosureis not limited to the embodiments described above, and includes allchanges and modifications without departing from the technical scope orthe essence of the present disclosure defined by the claims.

The invention claimed is:
 1. A driving control apparatus for anautomated driving vehicle, comprising: a LiDAR sensor configured tothree-dimensionally scan laser light outward of the vehicle and toreceive reflected light; an analyzer configured to groupthree-dimensional points of the reflected light acquired by the LiDARsensor into clusters; and a determination unit configured to compareshapes and reflectance distributions between at least one of theclusters obtained by the analyzer and a reference cluster representing astop target to determine whether or not the at least one clusterincludes the stop target, wherein: the reference cluster has a stripeconfiguration including repeated patterns of a plurality of regions inwhich adjacent regions have different reflectances with respect to thelaser light, the reference cluster includes, as the stripeconfiguration, a high reflectance region having a relatively highreflectance with respect to the laser light and a low reflectance regionhaving a relatively low reflectance with respect to the laser light, thehigh reflectance region and the low reflectance region being alternatelyarranged repeatedly, the reference cluster includes, as the stripeconfiguration, a horizontal stripe configuration including the highreflectance region and the low reflectance region both extendinghorizontally, the high reflectance region and the low reflectance regionbeing alternately disposed repeatedly in a vertical direction, and thereference cluster has a surface shape of 0° to 180° of a circularcylindrical side face.